<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>We propose a method for content-based retrieving solar magnetograms. We use the SDO Helioseismic and Magnetic Imager output collected with SunPy PyTorch libraries. We create a mathematical representation of the magnetic field regions of the Sun in the form of a vector. Thanks to this solution we can compare short vectors instead of comparing full-disk images. In order to decrease the retrieval time, we used a fully-connected autoencoder, which reduced the 256-element descriptor to a 32-element semantic hash. The performed experiments and comparisons proved the efficiency of the proposed approach. Our approach has the highest precision value in comparison with other state-of-the-art methods. The presented method can be used not only for solar image retrieval but also for classification tasks.</p>
</abstract>

We propose a method for content-based retrieving solar magnetograms. We use the SDO Helioseismic and Magnetic Imager output collected with SunPy PyTorch libraries. We create a mathematical representation of the magnetic field regions of the Sun in the form of a vector. Thanks to this solution we can compare short vectors instead of comparing full-disk images. In order to decrease the retrieval time, we used a fully-connected autoencoder, which reduced the 256-element descriptor to a 32-element semantic hash. The performed experiments and comparisons proved the efficiency of the proposed approach. Our approach has the highest precision value in comparison with other state-of-the-art methods. The presented method can be used not only for solar image retrieval but also for classification tasks.

Semantic Hashing for Fast Solar Magnetogram Retrieval

Department of Intelligent Computer Systems

University of Social Sciences ul. Sienkiewicza 9

Department of Computer, Control and Management Engineering

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Semantic Hashing for Fast Solar Magnetogram Retrieval"}

We propose a method for content-based retrieving solar magnetograms. We use the SDO Helioseismic and Magnetic Imager output collected with SunPy PyTorch...

Semantic Hashing for Fast Solar Magnetogram Retrieval

Online veröffentlicht: 29. Okt. 2022

Seitenbereich: 299 - 306

Eingereicht: 12. Mai 2022

Akzeptiert: 19. Okt. 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0020

Schlüsselwörter
Content-Based Image Retrieval, Image Descriptor, Solar Analysis

© 2022 Rafał Grycuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Semantic Hashing for Fast Solar Magnetogram Retrieval

Online veröffentlicht: 29. Okt. 2022

Seitenbereich: 299 - 306

Eingereicht: 12. Mai 2022

Akzeptiert: 19. Okt. 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0020

SchlüsselwörterContent-Based Image Retrieval, Image Descriptor, Solar Analysis

© 2022 Rafał Grycuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Content-Based Image Retrieval, Image Descriptor, Solar Analysis